Elena Toth

Panta Rhei-Everything Flows: Change in hydrology and society-The IAHS Scientific Decade 2013-2022

Abstract The new Scientific Decade 2013–2022 of IAHS, entitled “Panta Rhei—Everything Flows”, is dedicated to research activities on change in hydrology and society. The purpose of Panta Rhei is to reach an improved interpretation of the processes governing the water cycle by focusing on their changing dynamics in connection with rapidly changing human systems. The practical aim is to improve our capability to make predictions of water resources dynamics to support sustainable societal development in a changing environment. The concept implies a focus on hydrological systems as a changing interface between environment and society, whose dynamics are essential to determine water security, human safety and development, and to set priorities for environmental management. The Scientific Decade 2013–2022 will devise innovative theoretical blueprints for the representation of processes including change and will focus on advanced monitoring and data analysis techniques. Interdisciplinarity will be sought by increased efforts to connect with the socio-economic sciences and geosciences in general. This paper presents a summary of the Science Plan of Panta Rhei, its targets, research questions and expected outcomes. Editor Z.W. Kundzewicz Citation Montanari, A., Young, G., Savenije, H.H.G., Hughes, D., Wagener, T., Ren, L.L., Koutsoyiannis, D., Cudennec, C., Toth, E., Grimaldi, S., Blöschl, G., Sivapalan, M., Beven, K., Gupta, H., Hipsey, M., Schaefli, B., Arheimer, B., Boegh, E., Schymanski, S.J., Di Baldassarre, G., Yu, B., Hubert, P., Huang, Y., Schumann, A., Post, D., Srinivasan, V., Harman, C., Thompson, S., Rogger, M., Viglione, A., McMillan, H., Characklis, G., Pang, Z., and Belyaev, V., 2013. “Panta Rhei—Everything Flows”: Change in hydrology and society—The IAHS Scientific Decade 2013–2022. Hydrological Sciences Journal. 58 (6) 1256–1275.

Comparison of short-term rainfall prediction models for real-time flood forecasting

This study compares the accuracy of the short-term rainfall forecasts obtained with time-series analysis techniques, using past rainfall depths as the only input information. The techniques proposed here are linear stochastic auto-regressive movingaverage (ARMA) models, artificial neural networks (ANN) and the non-parametric nearest-neighbours method. The rainfall forecasts obtained using the considered methods are then routed through a lumped, conceptual, rainfall‐runoff model, thus implementing a coupled rainfall‐runoff forecasting procedure for a case study on the Apennines mountains, Italy. The study analyses and compares the relative advantages and limitations of each time-series analysis technique, used for issuing rainfall forecasts for lead-times varying from 1 to 6 h. The results also indicate how the considered time-series analysis techniques, and especially those based on the use of ANN, provide a significant improvement in the flood forecasting accuracy in comparison to the use of simple rainfall prediction approaches of heuristic type, which are often applied in hydrological practice. q 2000 Elsevier Science B.V. All rights reserved.

Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting

This paper traces two decades of neural network rainfall-runoff and streamflow modelling, collectively termed ‘river forecasting’. The field is now firmly established and the research community involved has much to offer hydrological science. First, however, it will be necessary to converge on more objective and consistent protocols for: selecting and treating inputs prior to model development; extracting physically meaningful insights from each proposed solution; and improving transparency in the benchmarking and reporting of experimental case studies. It is also clear that neural network river forecasting solutions will have limited appeal for operational purposes until confidence intervals can be attached to forecasts. Modular design, ensemble experiments, and hybridization with conventional hydrological models are yielding new tools for decision-making. The full potential for modelling complex hydrological systems, and for characterizing uncertainty, has yet to be realized. Further gains could also emerge from the provision of an agreed set of benchmark data sets and associated development of superior diagnostics for more rigorous intermodel evaluation. To achieve these goals will require a paradigm shift, such that the mass of individual isolated activities, focused on incremental technical refinement, is replaced by a more coordinated, problem-solving international research body.

Panta Rhei 2013–2015: global perspectives on hydrology, society and change

ABSTRACT In 2013, the International Association of Hydrological Sciences (IAHS) launched the hydrological decade 2013–2022 with the theme “Panta Rhei: Change in Hydrology and Society”. The decade recognizes the urgency of hydrological research to understand and predict the interactions of society and water, to support sustainable water resource use under changing climatic and environmental conditions. This paper reports on the first Panta Rhei biennium 2013–2015, providing a comprehensive resource that describes the scope and direction of Panta Rhei. We bring together the knowledge of all the Panta Rhei working groups, to summarize the most pressing research questions and how the hydrological community is progressing towards those goals. We draw out interconnections between different strands of research, and reflect on the need to take a global view on hydrology in the current era of human impacts and environmental change. Finally, we look back to the six driving science questions identified at the outset of Panta Rhei, to quantify progress towards those aims. Editor D. Koutsoyiannis; Associate editor not assigned

Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan

ABSTRACT “Panta Rhei – Everything Flows” is the science plan for the International Association of Hydrological Sciences scientific decade 2013–2023. It is founded on the need for improved understanding of the mutual, two-way interactions occurring at the interface of hydrology and society, and their role in influencing future hydrologic system change. It calls for strategic research effort focused on the delivery of coupled, socio-hydrologic models. In this paper we explore and synthesize opportunities and challenges that socio-hydrology presents for data-driven modelling. We highlight the potential for a new era of collaboration between data-driven and more physically-based modellers that should improve our ability to model and manage socio-hydrologic systems. Crucially, we approach data-driven, conceptual and physical modelling paradigms as being complementary rather than competing, positioning them along a continuum of modelling approaches that reflects the relative extent to which hypotheses and/or data are available to inform the model development process. EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR not assigned

Real-time flood forecasting via combined use of conceptual and stochastic models

Abstract Stochastic modelling of the simulation errors resulting from the off-line application of conceptual rainfall-runoff models is often performed in the context of real-time flood forecasting, in order to improve the forecasting accuracy. Although widely applied in the operational practice, such approach has not been yet extensively investigated in the scientific literature. This analysis is aimed at evaluating the benefits in discharge forecast accuracy that can be gained by this kind of approach and to provide some insights into the identification and estimation procedures of the optimal stochastic model to be applied when updating the forecasts. Application of univariate linear ARIMA models, even in the fractionally differenced form, has been herein considered for a case study referred to the Sieve River basin, located in Central Italy. The results highlight the dependence of the benefits retrievable from the stochastic updating procedure on the lead time of the flood forecasting.

Hydroinformatics: computational intelligence and technological developments in water science applications—Editorial

LINDA SEE, DIMITRI SOLOMATINE, ROBERT ABRAHART & ELENA TOTH 1School of Geography, University of Leeds, Leeds LS2 9JT, UK geolms@leeds.ac.uk 2UNESCO-IHE Institute for Water Education, PO Box 3015, 2601 DA Delft, The Netherlands d.solomatine@unesco-ihe.org 3School of Geography, University of Nottingham, Nottingham NG7 2RD, UK bob.abrahart@nottingham.ac.uk 4DISTART, Faculty of Engineering, University of Bologna, Bologna, Italy elena.toth@mail.ing.unibo.it

Adaptation of water resources systems to changing society and environment: a statement by the International Association of Hydrological Sciences

ABSTRACT We explore how to address the challenges of adaptation of water resources systems under changing conditions by supporting flexible, resilient and low-regret solutions, coupled with on-going monitoring and evaluation. This will require improved understanding of the linkages between biophysical and social aspects in order to better anticipate the possible future co-evolution of water systems and society. We also present a call to enhance the dialogue and foster the actions of governments, the international scientific community, research funding agencies and additional stakeholders in order to develop effective solutions to support water resources systems adaptation. Finally, we call the scientific community to a renewed and unified effort to deliver an innovative message to stakeholders. Water science is essential to resolve the water crisis, but the effectiveness of solutions depends, inter alia, on the capability of scientists to deliver a new, coherent and technical vision for the future development of water systems. EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR not assigned

Prediction of floods in ungauged basins

List of contributors Foreword Thomas Dunne Preface Gunter Bloschl, Murugesu Sivapalan, Thorsten Wagener, Alberto Viglione and Hubert Savenije 1. Introduction Gunter Bloschl, Murugesu Sivapalan, Thorsten Wagener, Alberto Viglione and Hubert Savenije 2. A synthesis framework for runoff predictions in ungauged basins Thorsten Wagener, Gunter Bloschl, David Goodrich, Hoshin V. Gupta, Murugesu Sivapalan, Yasuto Tachikawa, Peter Troch and Markus Weiler 3. A data acquisition framework for predictions of runoff in ungauged basins Brian McGlynn, Gunter Bloschl, Marco Borga, Helge Bormann, Ruud Hurkmans, Jurgen Komma, Lakshman Nandagiri, Remko Uijlenhoet and Thorsten Wagener 4. Process realism: flow paths and storage Dorthe Tetzlaff, Ghazi Al-Rawas, Gunter Bloschl, Sean K. Carey, Ying Fan, Markus Hrachowitz, Robert Kirnbauer, Graham Jewitt, Hjalmar Laudon, Kevin J. McGuire, Takahiro Sayama, Chris Soulsby, Erwin Zehe and Thorsten Wagener 5. Prediction of annual runoff in ungauged basins Thomas McMahon, Gregor Laaha, Juraj Parajka, Murray C. Peel, Hubert Savenije, Murugesu Sivapalan, Jan Szolgay, Sally Thompson, Alberto Viglione, Ross Woods and Dawen Yang 6. Prediction of seasonal runoff in ungauged basins R. Weingartner, Gunter Bloschl, David Hannah, Danny Marks, Juraj Parajka, Charles Pearson, Magdalena Rogger, Jose Luis. Salinas, Eric Sauquet, Sri Srikanthan, Sally Thompson and Alberto Viglione 7. Prediction of flow duration curves in ungauged basins Attilio Castellarin, Gianluca Botter, Denis A. Hughes, Suxia Liu, Taha B. M. J. Ouarda, Juraj Parajka, David Post, Murugesu Sivapalan, Christopher Spence, Alberto Viglione and Richard Vogel 8. Prediction of low flows in ungauged basins Gregor Laaha, Siegfried Demuth, Hege Hisdal, Charles N. Kroll, Henny A. J. van Lanen, Thomas Nester, Magdalena Rogger, Eric Sauquet, Lena M. Tallaksen, Ross Woods and Andy Young 9. Prediction of floods in ungauged basins Dan Rosbjerg, Gunter Bloschl, Donald H. Burn, Attilio Castellarin, Barry Croke, Guliano Di Baldassarre, Vito Iacobellis, Thomas Kjeldsen, George Kuczera, Ralf Merz, Alberto Montanari, David Morris, Taha B. M. J. Ouarda, Liliang Ren, Magdalena Rogger, Jose Luis Salinas, Elena Toth and Alberto Viglione 10. Predictions of runoff hydrographs in ungauged basins Juraj Parajka, Vazken Andreassian, Stacey Archfield, Andras Bardossy, Francis Chiew, Qingyun Duan, Alexander Gelfan, Kamila Hlavcova, Ralf Merz, Neil McIntyre, Ludovic Oudin, Charles Perrin, Magdalena Rogger, Jose Luis Salinas, Hubert Savenije, Jon Olav Skoien, Thorsten Wagener, Erwin Zehe and Yongqiang Zhang 11. Case studies Hubert Savenije, Murugesu Sivapalan, Trent Biggs, Shaofeng Jia, Leonid M. Korytny, E.A.Ilyichyova, Boris Gartsman, John W. Pomeroy, Kevin Shook, Xing Fang, Tom Brown, Denis A. Hughes, Stacey Archfield, Jos Samuel, Paulin Coulibaly, Robert A. Metcalfe, Attilio Castellarin, Ralf Merz, Gunter Humer, Ataur Rahman, Khaled Haddad, Erwin Weinmann, George Kuczera, Theresa Blume, Armand Crabit, Francois Colin, Roger Moussa, Hessel Winsemius, Hubert Savenije, Jens Liebe, Nick van de Giesen, M. Todd Walter, Tammo S. Steenhuis, Jeffrey R. Kennedy, David Goodrich, Carl L. Unkrich, Dominic Mazvimavi, Neil R. Viney, Kuniyoshi Takeuchi, H. A. P. Hapuarachchi, Anthony S. Kiem, Hiroshi Ishidaira, Tianqi Ao, Jun Magome, Maichun C. Zhou, Mikhail Georgievski, Guoqiang Wang, Chihiro Yoshimura, Berit Arheimer, Goran Lindstrom and Shijun Lin 12. Synthesis across processes, places and scales Hoshin V. Gupta, Gunter Bloschl, Jeffrey McDonnell, Hubert Savenije, Murugesu Sivapalan, Alberto Viglione and Thorsten Wagener 13. Recommendations Kuniyoshi Takeuchi, Gunter. Bloschl, Hubert Savenije, John Schaake, Murugesu Sivapalan, Alberto Viglione, Thorsten Wagener and Gordon Young Appendix: summary of studies used in the comparative assessments References Index.This book is devoted to predicting runoff in ungauged basins (PUB), i.e., predicting runoff at those locations where no runoff data are available. It aims at a synthesis of research on predictions of runoff in ungauged basins across processes, places and scales as a response to the dilemma of fragmentation in hydrology. It takes a comparative approach to learning from the differences and similarities between catchments around the world. The book also provides a comparative performance assessment (in the form of blind testing) of methods that are being used for predictions in ungauged basins, interpreted in a hydrologically meaningful way. It therefore throws light on the status of PUB at the present moment and can serve as a benchmark against which future progress on PUB can be judged. In so doing, the book has also come out with a new scientific framework that can guide the advances that are needed to underpin PUB and to advance the science of hydrology as a whole. The synthesis presented in the book is built on the collective experience of a large number of researchers around the world inspired by the PUB initiative of the International Association of Hydrological Sciences, which makes it truly a community effort. It has provided insights into the scientific, technical and societal factors that contribute to PUB. On the basis of the synthesis presented in this book, recommendations are made on the predictive, scientific and community aspects of PUB and of hydrology as a whole. www.cambridge.org

Asymmetric Error Functions for Reducing the Underestimation of Local Scour around Bridge Piers: Application to Neural Networks Models

AbstractMany of the empirical formulas used for the prediction of the expected scour depth at piers are excessively conservative, providing substantial overestimations. On the other hand, the recently proposed neural networks methods generally issue accurate predictions but also high percentages of underpredictions, due to the use of a symmetric error function for their parameterization. A novel error function is proposed in this paper for optimizing neural networks, giving more weight to underestimation than to overestimation discrepancies, in order to obtain safer design predictions. The performances of the proposed model on independent field records are compared with those of a conventionally trained neural network and with those of a set of widely used formulas. The asymmetric error function (that might be applied to parameterize any other model or equation, as a proficient alternative to least-square errors or envelope curves) allows researchers to obtain predictions closer to the measurements than t...